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Learning and reasoning in complex coalition information environments: a critical analysis

Cerutti, Federico ORCID:, Alzantot, Mustafa, Xing, Tianwei, Harborne, Daniel, Bakdash, Jonathan, Braines, Dave, Chakraborty, Supriyo, Kaplan, Lance, Kimmig, Angelika ORCID:, Preece, Alun David ORCID:, Raghavendra, Ramya, Sensoy, Murat and Srivastava, Mani 2018. Learning and reasoning in complex coalition information environments: a critical analysis. Presented at: Fusion 2018: 21st International Conference on Information Fusion, Cambridge, UK, 10-13 July 2018.

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In this paper we provide a critical analysis with met- rics that will inform guidelines for designing distributed systems for Collective Situational Understanding (CSU). CSU requires both collective insight—i.e., accurate and deep understanding of a situation derived from uncertain and often sparse data and collective foresight—i.e., the ability to predict what will happen in the future. When it comes to complex scenarios, the need for a distributed CSU naturally emerges, as a single monolithic approach not only is unfeasible: it is also undesirable. We therefore propose a principled, critical analysis of AI techniques that can support specific tasks for CSU to derive guidelines for designing distributed systems for CSU.

Item Type: Conference or Workshop Item (Paper)
Date Type: Completion
Status: Unpublished
Schools: Computer Science & Informatics
Crime and Security Research Institute (CSURI)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
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Date of First Compliant Deposit: 8 May 2018
Last Modified: 22 Nov 2022 09:58

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